Formal Statistical Replication Analysis in Lung Cancer Genome-Wide Association Studies
Bibliographic record
Abstract
Abstract Dozens of genome-wide association studies (GWAS) have identified thousands of single nucleotide polymor-phisms (SNPs) associated with lung cancer risk. However, it remains challenging to translate these findings to clinical insights. One well-known obstacle is the large amount of type I error attached to GWAS; attempted solutions such as setting a p -value threshold across multiple cohorts or looking for small meta-analysis p -values have only somewhat reduced false positive findings. In contrast, here we advocate for a statistical model-based replication analysis. We first demonstrate that a formal statistical test for the replication com-posite null hypothesis - i.e. that the regression coefficient of a SNP falls in the same direction in multiple cohorts simultaneously - can curate a smaller, higher-quality list of significant SNPs than common alterna-tives. In two-way simulations, the false discovery rate (FDR) of model-based replication analysis is 6.4 times lower than that of meta-analysis with a p < 10 −8 threshold. In three-way replication analysis, 9.8% of the International Lung Cancer Consortium GWAS significant SNPs are replicated for squamous cell lung cancer while 33.8% are replicated for lung adenocarcinoma. Finally, we construct polygenic risk scores (PRSs) and find the replication-based PRS achieves virtually identical performance to a GWAS-significant PRS while us-ing 87.3% fewer variants. Thus, formal model-based replication analysis can greatly reduce spurious findings while still identifying important variants, allowing for more robust and more efficient translation of GWAS results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.358 | 0.653 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".